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Understanding and Predicting Subseasonal Variations

Given that the details of daily weather are unpredictable beyond about a week, the questions of what aspects of the circulation remain predictable and what useful information can be extracted from predicting them present interesting challenges. The forecast problem is particularly difficult for Week 2, because boundary conditions have begun to become important but initial conditions have not yet completely lost their influence; at the same time, the chaos from unpredictable nonlinear interactions has nearly saturated. This is mainly why prediction efforts have traditionally focused on shorter (synoptic) and longer (seasonal to interannual) time scales. And yet there is much to said for shifting some of the focus to the subseasonal scale, if only because variability on this scale accounts for a large fraction of the total atmospheric variability from synoptic to decadal scales. Also, episodes of springtime floods, summertime droughts, and prolonged wet or dry spells are phenomena with obvious societal consequences.

CDC scientists are addressing these issues by focusing on the variability and predictability of weekly averages, through both modeling and diagnosis of the observed statistics, and through detailed investigations of NCEP's operational forecast ensembles for Week 2. A significant recent accomplishment was the construction of a low-dimensional 37-component linear empirical-dynamical model that not only successfully represents the statistics of weekly anomalies but also has comparable forecast skill in Week 2 to that of NCEP's operational ensemble. There is evidence that much of this model's skill arises from processes not well represented in the NCEP or other numerical models, such as subseasonal variations of tropical convection. On the other hand, much of the skill of the numerical models is likely due to processes not well represented in the empirical model, such as nonlinear baroclinic cyclogenesis or blocking development in Week 1. It is therefore possible that an intelligent combination of the empirical and numerical model forecasts will yield a Week 2 forecast that is superior to either in isolation. Constructing such a combination is now one of our primary efforts. This effort will benefit from and build on our recent success in improving both statistical and numerical forecast products for this time scale.

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